1. Field of the Invention
The present invention relates to a digital TV channel equalizer, and more particularly, to an apparatus for channel equalization and method thereof. Although the present invention is suitable for a wide scope of applications, it is particularly suitable for performing equalization in a frequency domain using conjugate-gradient algorithm.
2. Discussion of the Related Art
Generally, a digital transceiver system maps digital information (e.g., voice, data and video) of a transmitting end into symbols, converts each of the symbols to an analog signal proportional to a size or phase, and then transmits the analog signal to a receiving end over a transport channel. In doing so, interfering with a neighbor signal while passing through the transport channel of multi-path, the signal arriving at the receiving end is severely distorted. Hence, an equalizer is needed for channel compensation to restore an original signal from the distorted received signal.
Currently, as an equalizer mostly adopted by a receiver for a single carrier transmission system such as the U.S. terrestrial broadcasting, there is a non-linear decision feedback equalizer, which is explained with reference to FIG. 1 as follows.
FIG. 1 is a block diagram of a non-linear decision feedback equalizer according to a related art.
An operation of a non-linear decision feedback equalizer is explained with reference to FIG. 1.
First of all, after an influence of a pre-ghost that is a signal of a path arriving earlier than a main path via a feedforward (front-end) filter 101 has been removed, an influence of a post-ghost that is a signal of a path arriving later than a main path via a feedback (rear-end) filter 102 is removed.
In doing so, an adder 105 adds an output of the front-end filter 101 and an output of the feedback filter 102 and then outputs its output signal to a decision device 103. The decision device 103 compares the output signal of the adder 105 to a preset reference value to decide the output signal of the adder 105 as a signal level having a closest distance. In this case, an output of the decision device 103 becomes an input value to the feedback filter 102 and a controller 104.
If a decision of the decision device 103 is made accurately, the output signal is re-inputted as an input of the feedback filter 102 while noise included in an equalizer output component is removed to avoid noise amplification. Hence, the nonlinear decision feedback equalizer has performance better than a general linear equalizer.
Yet, in case that channel distortion is considerable, a decision error frequently occurs in a decision value that becomes an input of the feedback filter 102. And, an error propagation situation, in which the wrong decision value keeps circulating an infinite loop within the feedback filter 102 to degrade performance of an equalizer, may take place.
If the main path is blocked so that a signal received via a reflective path exists only or if a same signal is transmitted via different paths (single frequency network: SFN), a situation that incoming energy via each of the paths becomes similar may happen. So, it becomes unclear which signal will be taken as a main.
Namely, if positions of the main and reflective paths in a time-domain equalizer are frequently varied, performance degradation of the equalizer. So, it becomes impossible to perform channel decoding in a rear end of the equalizer in case of fluctuation of frame synchronization.
To solve such a problem, a zero forcing (ZF) frequency domain channel equalizer using a channel estimator and a noise predictor was proposed.
FIG. 2 is a block diagram of a ZF (zero forcing) frequency domain channel equalizer.
Referring to FIG. 2, a channel estimator 210 outputs an estimated channel ĥ(n) by accurately estimating a transport channel h(n) in viewpoint of a least square sense using a training signal inserted in a transmission signal.
The estimated channel ĥ(n) is transformed into a frequency domain Ĥ(w) in an FFT (fast Fourier transform) 222. The frequency domain Ĥ(w) is transformed into Ĥ−1(w) as a frequency response of a reverse channel via a ROM table 223. And, a complex multiplier 231 multiplies the Ĥ−1(w) by reception data Y(w) transformed into a frequency domain in the FFT 221.
An output of the complex multiplier 231 is reverse-transformed into a time domain again by an IFFT (inverse fast Fourier transform) 232, whereby a ZF type channel equalization end time domain data symbol is obtained.
Noise amplified in the process of equalization is removed by a noise remover 240 provided to a rear end of the equalizer. The noise-removed signal is determined by a decision device 250 to be outputted as a decision value closest to an output of the equalizer to a MUX 252.
The MUX 252 is a sort of selector that selects a training sequence of a training signal generator 251 in a training signal section or an output of the decision device 250 in a data section to output to a noise predictor 241.
The above-configured frequency domain ZF equalizer exhibits excellent performance in a static multi-path channel. Yet, since the channel estimator updates an equalizer coefficient by estimating a channel in a frame sync section only, the equalizer has a disadvantage that equalization is barely performed in a dynamic channel.
To compensate such a disadvantage, a frequency domain LMS equalizer has been proposed. The frequency domain LMS equalizer finds an equalizer coefficient from a frame sync using a channel estimator and enhances equalizing performance in a dynamic channel by updating the equalizer coefficient using LMS algorithm in a data section, which is explained with reference to the attached drawing as follows.
FIG. 3 is a block diagram of a frequency domain LMS equalizer.
Referring to FIG. 3, like the frequency domain ZF equalizer, a channel estimator 310 outputs an estimated channel ĥ(n) by accurately estimating a transport channel h(n) in viewpoint of a least square sense using a training signal inserted in a transmission signal. The estimated channel ĥ(n) is transformed into a frequency domain in an FFT (fast Fourier transform) 322. The frequency-domain-transformed estimated channel is transformed into Ĥ−1(w) as a frequency response of a reverse channel via a ROM table 323.
The Ĥ−1(w) is used as an initial coefficient of the frequency domain LMS equalizer. Namely, a channel is compensated in a manner of performing adaptive equalization on a transport channel distorted for a data section having the initial coefficient using LSM type algorithm.
For this, an LMS coefficient update unit 330 updates the equalizer coefficient using LMS algorithm in the data section. And, a next signal is used as an input value of the LMS coefficient update unit 330.
Namely, the data equalized in the frequency domain is inverse-transformed into a time domain via an IFFT 332 to obtain a channel equalization end time domain data symbol. And, the signal becomes an input signal of a noise predictor 341 to remove noise amplified in the equalization process.
The noise-removed clear signal becomes a decision value closest to an output of the equalizer via a decision device 350. And, an error in the equalizer output is found a MUX 352 using the decision value. The error is transformed into the frequency domain by an FFT 333 to become an input value of the LMS coefficient update unit 330 so that the equalizer coefficient in the frequency domain is updated.
Thus, the LMS equalizer is advantageous in performing channel equalization in a dynamic channel by updating the coefficient of the equalizer in the data section using the LMS algorithm. Yet, it is disadvantageous that the LMS algorithm has a slow convergence speed despite the advantage of stable convergence in an environment having a poor channel and a considerable noise.
Hence, the frequency domain equalizer using the LMS algorithm has performance better than that of the ZF frequency domain equalizer but fails in performing channel equalization in such a fast dynamic channel as a moving vehicle or a roadside.